Overview

Dataset statistics

Number of variables 16
Number of observations 48895
Missing cells 20141
Missing cells (%) 2.6%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 6.0 MiB
Average record size in memory 128.0 B

Variable types

Numeric 10
Text 3
Categorical 2
DateTime 1

Alerts

id is highly overall correlated with host_id High correlation
host_id is highly overall correlated with id High correlation
latitude is highly overall correlated with neighbourhood_group High correlation
longitude is highly overall correlated with neighbourhood_group High correlation
number_of_reviews is highly overall correlated with reviews_per_month High correlation
reviews_per_month is highly overall correlated with number_of_reviews High correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fields High correlation
last_review has 10052 (20.6%) missing values Missing
reviews_per_month has 10052 (20.6%) missing values Missing
minimum_nights is highly skewed (γ1 = 21.82727453) Skewed
id has unique values Unique
number_of_reviews has 10052 (20.6%) zeros Zeros
availability_365 has 17533 (35.9%) zeros Zeros

Reproduction

Analysis started 2023-07-11 10:01:06.682475
Analysis finished 2023-07-11 10:01:18.202211
Duration 11.52 seconds
Software version ydata-profiling vv4.3.1
Download configuration config.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct 48895
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 19017143
Minimum 2539
Maximum 36487245
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:18.430049 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 2539
5-th percentile 1222382.7
Q1 9471945
median 19677284
Q3 29152178
95-th percentile 35259101
Maximum 36487245
Range 36484706
Interquartile range (IQR) 19680234

Descriptive statistics

Standard deviation 10983108
Coefficient of variation (CV) 0.57753724
Kurtosis -1.2277483
Mean 19017143
Median Absolute Deviation (MAD) 9908242
Skewness -0.090257375
Sum 9.2984322 × 1011
Variance 1.2062867 × 1014
Monotonicity Strictly increasing
2023-07-11T11:01:18.537618 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
2539 1
 
< 0.1%
25583366 1
 
< 0.1%
25551687 1
 
< 0.1%
25552076 1
 
< 0.1%
25554120 1
 
< 0.1%
25568873 1
 
< 0.1%
25571627 1
 
< 0.1%
25572892 1
 
< 0.1%
25580113 1
 
< 0.1%
25580283 1
 
< 0.1%
Other values (48885) 48885
> 99.9%
Value Count Frequency (%)
2539 1
< 0.1%
2595 1
< 0.1%
3647 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5099 1
< 0.1%
5121 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
Value Count Frequency (%)
36487245 1
< 0.1%
36485609 1
< 0.1%
36485431 1
< 0.1%
36485057 1
< 0.1%
36484665 1
< 0.1%
36484363 1
< 0.1%
36484087 1
< 0.1%
36483152 1
< 0.1%
36483010 1
< 0.1%
36482809 1
< 0.1%

name
Text

Distinct 47905
Distinct (%) 98.0%
Missing 16
Missing (%) < 0.1%
Memory size 382.1 KiB
2023-07-11T11:01:18.726585 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Length

Max length 179
Median length 78
Mean length 36.911148
Min length 1

Characters and Unicode

Total characters 1804180
Distinct characters 776
Distinct categories 20 ?
Distinct scripts 11 ?
Distinct blocks 17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 47260 ?
Unique (%) 96.7%

Sample

1st row Clean & quiet apt home by the park
2nd row Skylit Midtown Castle
3rd row THE VILLAGE OF HARLEM....NEW YORK !
4th row Cozy Entire Floor of Brownstone
5th row Entire Apt: Spacious Studio/Loft by central park
Value Count Frequency (%)
in 16752
 
5.6%
room 10038
 
3.4%
8430
 
2.8%
bedroom 7601
 
2.5%
private 7158
 
2.4%
apartment 6695
 
2.2%
cozy 4991
 
1.7%
apt 4618
 
1.5%
brooklyn 4049
 
1.4%
studio 3988
 
1.3%
Other values (12552) 224301
75.1%
2023-07-11T11:01:19.051741 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
251424
 
13.9%
e 124635
 
6.9%
o 122324
 
6.8%
t 105261
 
5.8%
a 103586
 
5.7%
r 97946
 
5.4%
i 94651
 
5.2%
n 94611
 
5.2%
l 51723
 
2.9%
m 49121
 
2.7%
Other values (766) 708898
39.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1206208
66.9%
Uppercase Letter 270574
 
15.0%
Space Separator 251428
 
13.9%
Other Punctuation 33826
 
1.9%
Decimal Number 25321
 
1.4%
Dash Punctuation 6878
 
0.4%
Math Symbol 2738
 
0.2%
Other Letter 2547
 
0.1%
Close Punctuation 1537
 
0.1%
Open Punctuation 1395
 
0.1%
Other values (10) 1728
 
0.1%

Most frequent character per category

Other Letter
Value Count Frequency (%)
房 82
 
3.2%
家 46
 
1.8%
中 44
 
1.7%
间 41
 
1.6%
的 38
 
1.5%
拉 37
 
1.5%
法 36
 
1.4%
盛 36
 
1.4%
大 30
 
1.2%
约 29
 
1.1%
Other values (520) 2128
83.5%
Lowercase Letter
Value Count Frequency (%)
e 124635
 
10.3%
o 122324
 
10.1%
t 105261
 
8.7%
a 103586
 
8.6%
r 97946
 
8.1%
i 94651
 
7.8%
n 94611
 
7.8%
l 51723
 
4.3%
m 49121
 
4.1%
s 48092
 
4.0%
Other values (58) 314258
26.1%
Other Symbol
Value Count Frequency (%)
★ 266
30.3%
❤ 168
19.1%
☆ 105
 
11.9%
♥ 38
 
4.3%
⭐ 35
 
4.0%
✨ 34
 
3.9%
❥ 25
 
2.8%
✿ 15
 
1.7%
☀ 15
 
1.7%
✰ 14
 
1.6%
Other values (50) 164
18.7%
Uppercase Letter
Value Count Frequency (%)
B 29965
 
11.1%
S 26481
 
9.8%
C 20989
 
7.8%
A 19424
 
7.2%
R 17945
 
6.6%
P 14623
 
5.4%
E 14350
 
5.3%
L 14062
 
5.2%
M 11930
 
4.4%
N 11701
 
4.3%
Other values (33) 89104
32.9%
Other Punctuation
Value Count Frequency (%)
, 9177
27.1%
! 7855
23.2%
/ 5230
15.5%
. 4375
12.9%
& 3182
 
9.4%
' 1074
 
3.2%
* 1021
 
3.0%
: 597
 
1.8%
# 555
 
1.6%
" 294
 
0.9%
Other values (11) 466
 
1.4%
Math Symbol
Value Count Frequency (%)
+ 1382
50.5%
| 992
36.2%
~ 271
 
9.9%
= 34
 
1.2%
> 25
 
0.9%
< 20
 
0.7%
￫ 6
 
0.2%
⋆ 4
 
0.1%
√ 2
 
0.1%
× 1
 
< 0.1%
Decimal Number
Value Count Frequency (%)
1 8661
34.2%
2 6830
27.0%
3 2560
 
10.1%
5 2164
 
8.5%
0 2115
 
8.4%
4 1307
 
5.2%
6 569
 
2.2%
7 450
 
1.8%
8 399
 
1.6%
9 266
 
1.1%
Close Punctuation
Value Count Frequency (%)
) 1480
96.3%
] 37
 
2.4%
} 9
 
0.6%
】 8
 
0.5%
》 3
 
0.2%
Open Punctuation
Value Count Frequency (%)
( 1339
96.0%
[ 36
 
2.6%
{ 9
 
0.6%
【 8
 
0.6%
《 3
 
0.2%
Dash Punctuation
Value Count Frequency (%)
- 6804
98.9%
— 47
 
0.7%
– 26
 
0.4%
― 1
 
< 0.1%
Modifier Letter
Value Count Frequency (%)
ﾞ 21
56.8%
ｰ 11
29.7%
ﾟ 5
 
13.5%
Modifier Symbol
Value Count Frequency (%)
^ 9
56.2%
` 4
25.0%
´ 3
 
18.8%
Space Separator
Value Count Frequency (%)
251424
> 99.9%
  4
 
< 0.1%
Final Punctuation
Value Count Frequency (%)
’ 200
84.0%
” 38
 
16.0%
Nonspacing Mark
Value Count Frequency (%)
️ 165
92.2%
︎ 14
 
7.8%
Connector Punctuation
Value Count Frequency (%)
_ 42
97.7%
‿ 1
 
2.3%
Initial Punctuation
Value Count Frequency (%)
“ 40
83.3%
‘ 8
 
16.7%
Control
Value Count Frequency (%)
185
100.0%
Currency Symbol
Value Count Frequency (%)
$ 94
100.0%
Other Number
Value Count Frequency (%)
² 9
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1476579
81.8%
Common 324672
 
18.0%
Han 2237
 
0.1%
Cyrillic 191
 
< 0.1%
Inherited 179
 
< 0.1%
Katakana 136
 
< 0.1%
Hiragana 70
 
< 0.1%
Hangul 70
 
< 0.1%
Hebrew 31
 
< 0.1%
Georgian 13
 
< 0.1%

Most frequent character per script

Han
Value Count Frequency (%)
房 82
 
3.7%
家 46
 
2.1%
中 44
 
2.0%
间 41
 
1.8%
的 38
 
1.7%
拉 37
 
1.7%
法 36
 
1.6%
盛 36
 
1.6%
大 30
 
1.3%
约 29
 
1.3%
Other values (401) 1818
81.3%
Common
Value Count Frequency (%)
251424
77.4%
, 9177
 
2.8%
1 8661
 
2.7%
! 7855
 
2.4%
2 6830
 
2.1%
- 6804
 
2.1%
/ 5230
 
1.6%
. 4375
 
1.3%
& 3182
 
1.0%
3 2560
 
0.8%
Other values (123) 18574
 
5.7%
Latin
Value Count Frequency (%)
e 124635
 
8.4%
o 122324
 
8.3%
t 105261
 
7.1%
a 103586
 
7.0%
r 97946
 
6.6%
i 94651
 
6.4%
n 94611
 
6.4%
l 51723
 
3.5%
m 49121
 
3.3%
s 48092
 
3.3%
Other values (68) 584629
39.6%
Hangul
Value Count Frequency (%)
한 7
 
10.0%
웃 3
 
4.3%
성 3
 
4.3%
리 2
 
2.9%
맨 2
 
2.9%
건 2
 
2.9%
물 2
 
2.9%
은 2
 
2.9%
작 2
 
2.9%
뜻 2
 
2.9%
Other values (38) 43
61.4%
Cyrillic
Value Count Frequency (%)
а 26
13.6%
о 18
 
9.4%
т 17
 
8.9%
н 15
 
7.9%
е 13
 
6.8%
р 11
 
5.8%
к 11
 
5.8%
м 10
 
5.2%
в 9
 
4.7%
с 9
 
4.7%
Other values (23) 52
27.2%
Katakana
Value Count Frequency (%)
ﾝ 14
 
10.3%
ｸ 12
 
8.8%
ﾘ 10
 
7.4%
ﾊ 9
 
6.6%
ｯ 9
 
6.6%
ｱ 9
 
6.6%
ｽ 8
 
5.9%
ﾄ 7
 
5.1%
ｳ 6
 
4.4%
ﾌ 6
 
4.4%
Other values (22) 46
33.8%
Hiragana
Value Count Frequency (%)
の 16
22.9%
で 7
10.0%
か 7
10.0%
ら 6
 
8.6%
お 5
 
7.1%
い 4
 
5.7%
な 4
 
5.7%
に 3
 
4.3%
く 2
 
2.9%
き 2
 
2.9%
Other values (13) 14
20.0%
Hebrew
Value Count Frequency (%)
ו 5
16.1%
י 5
16.1%
ר 4
12.9%
ב 4
12.9%
ת 2
 
6.5%
ע 2
 
6.5%
ה 2
 
6.5%
ד 1
 
3.2%
ש 1
 
3.2%
ל 1
 
3.2%
Other values (4) 4
12.9%
Inherited
Value Count Frequency (%)
️ 165
92.2%
︎ 14
 
7.8%
Georgian
Value Count Frequency (%)
ღ 13
100.0%
Devanagari
Value Count Frequency (%)
ॐ 2
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1799687
99.8%
CJK 2237
 
0.1%
Misc Symbols 500
 
< 0.1%
None 431
 
< 0.1%
Punctuation 423
 
< 0.1%
Dingbats 320
 
< 0.1%
Cyrillic 191
 
< 0.1%
VS 179
 
< 0.1%
Hiragana 70
 
< 0.1%
Hangul 70
 
< 0.1%
Other values (7) 72
 
< 0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
251424
 
14.0%
e 124635
 
6.9%
o 122324
 
6.8%
t 105261
 
5.8%
a 103586
 
5.8%
r 97946
 
5.4%
i 94651
 
5.3%
n 94611
 
5.3%
l 51723
 
2.9%
m 49121
 
2.7%
Other values (86) 704405
39.1%
Misc Symbols
Value Count Frequency (%)
★ 266
53.2%
☆ 105
 
21.0%
♥ 38
 
7.6%
☀ 15
 
3.0%
♀ 11
 
2.2%
⚡ 8
 
1.6%
♦ 6
 
1.2%
♡ 6
 
1.2%
♛ 6
 
1.2%
⚓ 6
 
1.2%
Other values (12) 33
 
6.6%
Punctuation
Value Count Frequency (%)
’ 200
47.3%
• 62
 
14.7%
— 47
 
11.1%
“ 40
 
9.5%
” 38
 
9.0%
– 26
 
6.1%
‘ 8
 
1.9%
‿ 1
 
0.2%
― 1
 
0.2%
Dingbats
Value Count Frequency (%)
❤ 168
52.5%
✨ 34
 
10.6%
❥ 25
 
7.8%
✿ 15
 
4.7%
✰ 14
 
4.4%
✴ 11
 
3.4%
✪ 8
 
2.5%
✌ 6
 
1.9%
➡ 5
 
1.6%
✺ 4
 
1.2%
Other values (13) 30
 
9.4%
VS
Value Count Frequency (%)
️ 165
92.2%
︎ 14
 
7.8%
CJK
Value Count Frequency (%)
房 82
 
3.7%
家 46
 
2.1%
中 44
 
2.0%
间 41
 
1.8%
的 38
 
1.7%
拉 37
 
1.7%
法 36
 
1.6%
盛 36
 
1.6%
大 30
 
1.3%
约 29
 
1.3%
Other values (401) 1818
81.3%
None
Value Count Frequency (%)
⭐ 35
 
8.1%
à 28
 
6.5%
ó 24
 
5.6%
ﾞ 21
 
4.9%
é 16
 
3.7%
｡ 15
 
3.5%
ﾝ 14
 
3.2%
· 13
 
3.0%
ｸ 12
 
2.8%
ｰ 11
 
2.6%
Other values (70) 242
56.1%
Cyrillic
Value Count Frequency (%)
а 26
13.6%
о 18
 
9.4%
т 17
 
8.9%
н 15
 
7.9%
е 13
 
6.8%
р 11
 
5.8%
к 11
 
5.8%
м 10
 
5.2%
в 9
 
4.7%
с 9
 
4.7%
Other values (23) 52
27.2%
Hiragana
Value Count Frequency (%)
の 16
22.9%
で 7
10.0%
か 7
10.0%
ら 6
 
8.6%
お 5
 
7.1%
い 4
 
5.7%
な 4
 
5.7%
に 3
 
4.3%
く 2
 
2.9%
き 2
 
2.9%
Other values (13) 14
20.0%
Georgian
Value Count Frequency (%)
ღ 13
100.0%
Hangul
Value Count Frequency (%)
한 7
 
10.0%
웃 3
 
4.3%
성 3
 
4.3%
리 2
 
2.9%
맨 2
 
2.9%
건 2
 
2.9%
물 2
 
2.9%
은 2
 
2.9%
작 2
 
2.9%
뜻 2
 
2.9%
Other values (38) 43
61.4%
Hebrew
Value Count Frequency (%)
ו 5
16.1%
י 5
16.1%
ר 4
12.9%
ב 4
12.9%
ת 2
 
6.5%
ע 2
 
6.5%
ה 2
 
6.5%
ד 1
 
3.2%
ש 1
 
3.2%
ל 1
 
3.2%
Other values (4) 4
12.9%
Misc Technical
Value Count Frequency (%)
⍟ 4
57.1%
⏪ 1
 
14.3%
⏩ 1
 
14.3%
⌚ 1
 
14.3%
Geometric Shapes
Value Count Frequency (%)
▲ 4
36.4%
◈ 2
18.2%
△ 2
18.2%
◔ 2
18.2%
▶ 1
 
9.1%
Math Operators
Value Count Frequency (%)
⋆ 4
57.1%
√ 2
28.6%
⊹ 1
 
14.3%
Devanagari
Value Count Frequency (%)
ॐ 2
100.0%
Letterlike Symbols
Value Count Frequency (%)
™ 1
100.0%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct 37457
Distinct (%) 76.6%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 67620011
Minimum 2438
Maximum 2.7432131 × 108
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:19.173285 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 2438
5-th percentile 815564.1
Q1 7822033
median 30793816
Q3 1.0743442 × 108
95-th percentile 2.417646 × 108
Maximum 2.7432131 × 108
Range 2.7431888 × 108
Interquartile range (IQR) 99612390

Descriptive statistics

Standard deviation 78610967
Coefficient of variation (CV) 1.16254
Kurtosis 0.16910576
Mean 67620011
Median Absolute Deviation (MAD) 27543913
Skewness 1.2062139
Sum 3.3062804 × 1012
Variance 6.1796841 × 1015
Monotonicity Not monotonic
2023-07-11T11:01:19.275093 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
219517861 327
 
0.7%
107434423 232
 
0.5%
30283594 121
 
0.2%
137358866 103
 
0.2%
16098958 96
 
0.2%
12243051 96
 
0.2%
61391963 91
 
0.2%
22541573 87
 
0.2%
200380610 65
 
0.1%
7503643 52
 
0.1%
Other values (37447) 47625
97.4%
Value Count Frequency (%)
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2787 6
< 0.1%
2845 2
 
< 0.1%
2868 1
 
< 0.1%
2881 2
 
< 0.1%
3151 1
 
< 0.1%
3211 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
Value Count Frequency (%)
274321313 1
< 0.1%
274311461 1
< 0.1%
274307600 1
< 0.1%
274298453 1
< 0.1%
274273284 1
< 0.1%
274225617 1
< 0.1%
274195458 1
< 0.1%
274188386 1
< 0.1%
274103383 1
< 0.1%
274079964 1
< 0.1%

host_name
Text

Distinct 11452
Distinct (%) 23.4%
Missing 21
Missing (%) < 0.1%
Memory size 382.1 KiB
2023-07-11T11:01:19.428709 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Length

Max length 35
Median length 31
Mean length 6.1248721
Min length 1

Characters and Unicode

Total characters 299347
Distinct characters 204
Distinct categories 15 ?
Distinct scripts 7 ?
Distinct blocks 9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 6903 ?
Unique (%) 14.1%

Sample

1st row John
2nd row Jennifer
3rd row Elisabeth
4th row LisaRoxanne
5th row Laura
Value Count Frequency (%)
1120
 
2.1%
and 625
 
1.1%
michael 460
 
0.8%
david 449
 
0.8%
sonder 423
 
0.8%
nyc 338
 
0.6%
john 337
 
0.6%
alex 330
 
0.6%
laura 293
 
0.5%
maria 244
 
0.4%
Other values (10259) 49968
91.5%
2023-07-11T11:01:19.711641 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
a 37929
 
12.7%
e 28680
 
9.6%
i 24284
 
8.1%
n 24092
 
8.0%
r 17861
 
6.0%
l 15327
 
5.1%
o 12743
 
4.3%
t 9401
 
3.1%
s 9147
 
3.1%
h 9040
 
3.0%
Other values (194) 110843
37.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 235916
78.8%
Uppercase Letter 54823
 
18.3%
Space Separator 5811
 
1.9%
Other Punctuation 1592
 
0.5%
Open Punctuation 381
 
0.1%
Close Punctuation 379
 
0.1%
Dash Punctuation 209
 
0.1%
Other Letter 110
 
< 0.1%
Decimal Number 84
 
< 0.1%
Math Symbol 34
 
< 0.1%
Other values (5) 8
 
< 0.1%

Most frequent character per category

Other Letter
Value Count Frequency (%)
明 6
 
5.5%
德 5
 
4.5%
青 5
 
4.5%
美 5
 
4.5%
文 4
 
3.6%
铀 3
 
2.7%
春 3
 
2.7%
常 3
 
2.7%
辣 2
 
1.8%
柏 2
 
1.8%
Other values (62) 72
65.5%
Lowercase Letter
Value Count Frequency (%)
a 37929
16.1%
e 28680
12.2%
i 24284
10.3%
n 24092
10.2%
r 17861
 
7.6%
l 15327
 
6.5%
o 12743
 
5.4%
t 9401
 
4.0%
s 9147
 
3.9%
h 9040
 
3.8%
Other values (54) 47412
20.1%
Uppercase Letter
Value Count Frequency (%)
A 6458
11.8%
J 5458
 
10.0%
M 5298
 
9.7%
S 4744
 
8.7%
C 3737
 
6.8%
L 2885
 
5.3%
D 2752
 
5.0%
K 2618
 
4.8%
R 2566
 
4.7%
E 2361
 
4.3%
Other values (28) 15946
29.1%
Decimal Number
Value Count Frequency (%)
5 20
23.8%
7 14
16.7%
0 14
16.7%
2 11
13.1%
4 7
 
8.3%
1 7
 
8.3%
6 4
 
4.8%
3 4
 
4.8%
8 2
 
2.4%
9 1
 
1.2%
Other Punctuation
Value Count Frequency (%)
& 1162
73.0%
. 309
 
19.4%
/ 41
 
2.6%
, 35
 
2.2%
' 25
 
1.6%
@ 8
 
0.5%
" 6
 
0.4%
! 4
 
0.3%
: 2
 
0.1%
Space Separator
Value Count Frequency (%)
5805
99.9%
  6
 
0.1%
Open Punctuation
Value Count Frequency (%)
( 381
100.0%
Close Punctuation
Value Count Frequency (%)
) 379
100.0%
Dash Punctuation
Value Count Frequency (%)
- 209
100.0%
Math Symbol
Value Count Frequency (%)
+ 34
100.0%
Other Symbol
Value Count Frequency (%)
★ 2
100.0%
Final Punctuation
Value Count Frequency (%)
’ 2
100.0%
Format
Value Count Frequency (%)
​ 2
100.0%
Connector Punctuation
Value Count Frequency (%)
_ 1
100.0%
Currency Symbol
Value Count Frequency (%)
£ 1
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 290683
97.1%
Common 8498
 
2.8%
Han 91
 
< 0.1%
Cyrillic 56
 
< 0.1%
Hangul 11
 
< 0.1%
Hebrew 5
 
< 0.1%
Hiragana 3
 
< 0.1%

Most frequent character per script

Latin
Value Count Frequency (%)
a 37929
 
13.0%
e 28680
 
9.9%
i 24284
 
8.4%
n 24092
 
8.3%
r 17861
 
6.1%
l 15327
 
5.3%
o 12743
 
4.4%
t 9401
 
3.2%
s 9147
 
3.1%
h 9040
 
3.1%
Other values (70) 102179
35.2%
Han
Value Count Frequency (%)
明 6
 
6.6%
德 5
 
5.5%
青 5
 
5.5%
美 5
 
5.5%
文 4
 
4.4%
铀 3
 
3.3%
春 3
 
3.3%
常 3
 
3.3%
辣 2
 
2.2%
柏 2
 
2.2%
Other values (45) 53
58.2%
Common
Value Count Frequency (%)
5805
68.3%
& 1162
 
13.7%
( 381
 
4.5%
) 379
 
4.5%
. 309
 
3.6%
- 209
 
2.5%
/ 41
 
0.5%
, 35
 
0.4%
+ 34
 
0.4%
' 25
 
0.3%
Other values (20) 118
 
1.4%
Cyrillic
Value Count Frequency (%)
а 6
10.7%
е 6
10.7%
н 6
10.7%
л 4
 
7.1%
А 4
 
7.1%
и 4
 
7.1%
р 3
 
5.4%
й 3
 
5.4%
с 3
 
5.4%
к 3
 
5.4%
Other values (12) 14
25.0%
Hangul
Value Count Frequency (%)
소 2
18.2%
정 2
18.2%
비 1
9.1%
나 1
9.1%
빈 1
9.1%
단 1
9.1%
진 1
9.1%
선 1
9.1%
현 1
9.1%
Hebrew
Value Count Frequency (%)
י 1
20.0%
ד 1
20.0%
נ 1
20.0%
ל 1
20.0%
א 1
20.0%
Hiragana
Value Count Frequency (%)
あ 1
33.3%
り 1
33.3%
ゆ 1
33.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 298922
99.9%
None 247
 
0.1%
CJK 91
 
< 0.1%
Cyrillic 56
 
< 0.1%
Hangul 11
 
< 0.1%
Punctuation 10
 
< 0.1%
Hebrew 5
 
< 0.1%
Hiragana 3
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 37929
 
12.7%
e 28680
 
9.6%
i 24284
 
8.1%
n 24092
 
8.1%
r 17861
 
6.0%
l 15327
 
5.1%
o 12743
 
4.3%
t 9401
 
3.1%
s 9147
 
3.1%
h 9040
 
3.0%
Other values (67) 110418
36.9%
None
Value Count Frequency (%)
é 107
43.3%
í 24
 
9.7%
á 22
 
8.9%
ú 19
 
7.7%
ë 13
 
5.3%
ô 11
 
4.5%
ó 9
 
3.6%
è 7
 
2.8%
ç 5
 
2.0%
ı 4
 
1.6%
Other values (19) 26
 
10.5%
Cyrillic
Value Count Frequency (%)
а 6
10.7%
е 6
10.7%
н 6
10.7%
л 4
 
7.1%
А 4
 
7.1%
и 4
 
7.1%
р 3
 
5.4%
й 3
 
5.4%
с 3
 
5.4%
к 3
 
5.4%
Other values (12) 14
25.0%
CJK
Value Count Frequency (%)
明 6
 
6.6%
德 5
 
5.5%
青 5
 
5.5%
美 5
 
5.5%
文 4
 
4.4%
铀 3
 
3.3%
春 3
 
3.3%
常 3
 
3.3%
辣 2
 
2.2%
柏 2
 
2.2%
Other values (45) 53
58.2%
Punctuation
Value Count Frequency (%)
  6
60.0%
’ 2
 
20.0%
​ 2
 
20.0%
Misc Symbols
Value Count Frequency (%)
★ 2
100.0%
Hangul
Value Count Frequency (%)
소 2
18.2%
정 2
18.2%
비 1
9.1%
나 1
9.1%
빈 1
9.1%
단 1
9.1%
진 1
9.1%
선 1
9.1%
현 1
9.1%
Hebrew
Value Count Frequency (%)
י 1
20.0%
ד 1
20.0%
נ 1
20.0%
ל 1
20.0%
א 1
20.0%
Hiragana
Value Count Frequency (%)
あ 1
33.3%
り 1
33.3%
ゆ 1
33.3%

neighbourhood_group
Categorical

HIGH CORRELATION 

Distinct 5
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 382.1 KiB
Manhattan
21661 
Brooklyn
20104 
Queens
5666 
Bronx
 
1091
Staten Island
 
373

Length

Max length 13
Median length 9
Mean length 8.1824522
Min length 5

Characters and Unicode

Total characters 400081
Distinct characters 20
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Brooklyn
2nd row Manhattan
3rd row Manhattan
4th row Brooklyn
5th row Manhattan

Common Values

Value Count Frequency (%)
Manhattan 21661
44.3%
Brooklyn 20104
41.1%
Queens 5666
 
11.6%
Bronx 1091
 
2.2%
Staten Island 373
 
0.8%

Length

2023-07-11T11:01:19.824850 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T11:01:19.927374 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Value Count Frequency (%)
manhattan 21661
44.0%
brooklyn 20104
40.8%
queens 5666
 
11.5%
bronx 1091
 
2.2%
staten 373
 
0.8%
island 373
 
0.8%

Most occurring characters

Value Count Frequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (10) 51763
12.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 350440
87.6%
Uppercase Letter 49268
 
12.3%
Space Separator 373
 
0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
n 70929
20.2%
a 65729
18.8%
t 44068
12.6%
o 41299
11.8%
h 21661
 
6.2%
r 21195
 
6.0%
l 20477
 
5.8%
y 20104
 
5.7%
k 20104
 
5.7%
e 11705
 
3.3%
Other values (4) 13169
 
3.8%
Uppercase Letter
Value Count Frequency (%)
M 21661
44.0%
B 21195
43.0%
Q 5666
 
11.5%
S 373
 
0.8%
I 373
 
0.8%
Space Separator
Value Count Frequency (%)
373
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 399708
99.9%
Common 373
 
0.1%

Most frequent character per script

Latin
Value Count Frequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (9) 51390
12.9%
Common
Value Count Frequency (%)
373
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 400081
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
n 70929
17.7%
a 65729
16.4%
t 44068
11.0%
o 41299
10.3%
M 21661
 
5.4%
h 21661
 
5.4%
B 21195
 
5.3%
r 21195
 
5.3%
l 20477
 
5.1%
y 20104
 
5.0%
Other values (10) 51763
12.9%

neighbourhood
Text

Distinct 221
Distinct (%) 0.5%
Missing 0
Missing (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:20.075430 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Length

Max length 26
Median length 17
Mean length 11.894795
Min length 4

Characters and Unicode

Total characters 581596
Distinct characters 54
Distinct categories 5 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 6 ?
Unique (%) < 0.1%

Sample

1st row Kensington
2nd row Midtown
3rd row Harlem
4th row Clinton Hill
5th row East Harlem
Value Count Frequency (%)
east 6592
 
8.3%
side 4680
 
5.9%
williamsburg 3920
 
5.0%
harlem 3775
 
4.8%
upper 3769
 
4.8%
bedford-stuyvesant 3714
 
4.7%
heights 3586
 
4.5%
village 3164
 
4.0%
west 2759
 
3.5%
bushwick 2465
 
3.1%
Other values (233) 40681
51.4%
2023-07-11T11:01:20.336461 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
e 53470
 
9.2%
i 42282
 
7.3%
s 39625
 
6.8%
t 38587
 
6.6%
a 37608
 
6.5%
l 34448
 
5.9%
r 33667
 
5.8%
30210
 
5.2%
n 26099
 
4.5%
o 24032
 
4.1%
Other values (44) 221568
38.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 461107
79.3%
Uppercase Letter 83934
 
14.4%
Space Separator 30210
 
5.2%
Dash Punctuation 4251
 
0.7%
Other Punctuation 2094
 
0.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 53470
11.6%
i 42282
 
9.2%
s 39625
 
8.6%
t 38587
 
8.4%
a 37608
 
8.2%
l 34448
 
7.5%
r 33667
 
7.3%
n 26099
 
5.7%
o 24032
 
5.2%
d 19663
 
4.3%
Other values (15) 111626
24.2%
Uppercase Letter
Value Count Frequency (%)
H 11901
14.2%
S 11483
13.7%
B 8374
10.0%
W 8185
9.8%
E 7084
8.4%
C 5327
 
6.3%
U 3833
 
4.6%
G 3723
 
4.4%
F 3281
 
3.9%
V 3209
 
3.8%
Other values (14) 17534
20.9%
Other Punctuation
Value Count Frequency (%)
' 1968
94.0%
. 124
 
5.9%
, 2
 
0.1%
Space Separator
Value Count Frequency (%)
30210
100.0%
Dash Punctuation
Value Count Frequency (%)
- 4251
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 545041
93.7%
Common 36555
 
6.3%

Most frequent character per script

Latin
Value Count Frequency (%)
e 53470
 
9.8%
i 42282
 
7.8%
s 39625
 
7.3%
t 38587
 
7.1%
a 37608
 
6.9%
l 34448
 
6.3%
r 33667
 
6.2%
n 26099
 
4.8%
o 24032
 
4.4%
d 19663
 
3.6%
Other values (39) 195560
35.9%
Common
Value Count Frequency (%)
30210
82.6%
- 4251
 
11.6%
' 1968
 
5.4%
. 124
 
0.3%
, 2
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 581596
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 53470
 
9.2%
i 42282
 
7.3%
s 39625
 
6.8%
t 38587
 
6.6%
a 37608
 
6.5%
l 34448
 
5.9%
r 33667
 
5.8%
30210
 
5.2%
n 26099
 
4.5%
o 24032
 
4.1%
Other values (44) 221568
38.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct 19048
Distinct (%) 39.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 40.728949
Minimum 40.49979
Maximum 40.91306
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:20.458436 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 40.49979
5-th percentile 40.646114
Q1 40.6901
median 40.72307
Q3 40.763115
95-th percentile 40.825643
Maximum 40.91306
Range 0.41327
Interquartile range (IQR) 0.073015

Descriptive statistics

Standard deviation 0.054530078
Coefficient of variation (CV) 0.0013388531
Kurtosis 0.14884466
Mean 40.728949
Median Absolute Deviation (MAD) 0.03642
Skewness 0.23716656
Sum 1991442
Variance 0.0029735294
Monotonicity Not monotonic
2023-07-11T11:01:20.569167 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
40.71813 18
 
< 0.1%
40.68444 13
 
< 0.1%
40.69414 13
 
< 0.1%
40.68634 13
 
< 0.1%
40.76125 12
 
< 0.1%
40.68537 12
 
< 0.1%
40.71171 12
 
< 0.1%
40.71353 12
 
< 0.1%
40.76189 12
 
< 0.1%
40.68683 11
 
< 0.1%
Other values (19038) 48767
99.7%
Value Count Frequency (%)
40.49979 1
< 0.1%
40.50641 1
< 0.1%
40.50708 1
< 0.1%
40.50868 1
< 0.1%
40.50873 1
< 0.1%
40.50943 1
< 0.1%
40.51133 1
< 0.1%
40.52211 1
< 0.1%
40.52293 1
< 0.1%
40.527 1
< 0.1%
Value Count Frequency (%)
40.91306 1
< 0.1%
40.91234 1
< 0.1%
40.91169 1
< 0.1%
40.91167 1
< 0.1%
40.90804 1
< 0.1%
40.90734 1
< 0.1%
40.90527 1
< 0.1%
40.90484 1
< 0.1%
40.90406 1
< 0.1%
40.90391 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct 14718
Distinct (%) 30.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -73.95217
Minimum -74.24442
Maximum -73.71299
Zeros 0
Zeros (%) 0.0%
Negative 48895
Negative (%) 100.0%
Memory size 382.1 KiB
2023-07-11T11:01:20.680490 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum -74.24442
5-th percentile -74.00388
Q1 -73.98307
median -73.95568
Q3 -73.936275
95-th percentile -73.865771
Maximum -73.71299
Range 0.53143
Interquartile range (IQR) 0.046795

Descriptive statistics

Standard deviation 0.046156736
Coefficient of variation (CV) -0.0006241431
Kurtosis 5.0216461
Mean -73.95217
Median Absolute Deviation (MAD) 0.02485
Skewness 1.2842102
Sum -3615891.3
Variance 0.0021304443
Monotonicity Not monotonic
2023-07-11T11:01:20.797260 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-73.95677 18
 
< 0.1%
-73.95427 18
 
< 0.1%
-73.95405 17
 
< 0.1%
-73.9506 16
 
< 0.1%
-73.94791 16
 
< 0.1%
-73.95332 16
 
< 0.1%
-73.95136 16
 
< 0.1%
-73.95669 15
 
< 0.1%
-73.95742 15
 
< 0.1%
-73.94537 15
 
< 0.1%
Other values (14708) 48733
99.7%
Value Count Frequency (%)
-74.24442 1
< 0.1%
-74.24285 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 1
< 0.1%
-74.23914 1
< 0.1%
-74.23803 1
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
-74.21017 1
< 0.1%
-74.20941 1
< 0.1%
Value Count Frequency (%)
-73.71299 1
< 0.1%
-73.7169 1
< 0.1%
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.71928 1
< 0.1%
-73.72173 1
< 0.1%
-73.72179 1
< 0.1%
-73.72247 1
< 0.1%
-73.72435 1
< 0.1%
-73.72581 1
< 0.1%

room_type
Categorical

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 382.1 KiB
Entire home/apt
25409 
Private room
22326 
Shared room
 
1160

Length

Max length 15
Median length 15
Mean length 13.535269
Min length 11

Characters and Unicode

Total characters 661807
Distinct characters 17
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Private room
2nd row Entire home/apt
3rd row Private room
4th row Entire home/apt
5th row Entire home/apt

Common Values

Value Count Frequency (%)
Entire home/apt 25409
52.0%
Private room 22326
45.7%
Shared room 1160
 
2.4%

Length

2023-07-11T11:01:20.900947 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T11:01:20.999187 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Value Count Frequency (%)
entire 25409
26.0%
home/apt 25409
26.0%
room 23486
24.0%
private 22326
22.8%
shared 1160
 
1.2%

Most occurring characters

Value Count Frequency (%)
e 74304
11.2%
t 73144
11.1%
o 72381
10.9%
r 72381
10.9%
a 48895
 
7.4%
48895
 
7.4%
m 48895
 
7.4%
i 47735
 
7.2%
h 26569
 
4.0%
p 25409
 
3.8%
Other values (7) 123199
18.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 538608
81.4%
Space Separator 48895
 
7.4%
Uppercase Letter 48895
 
7.4%
Other Punctuation 25409
 
3.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 74304
13.8%
t 73144
13.6%
o 72381
13.4%
r 72381
13.4%
a 48895
9.1%
m 48895
9.1%
i 47735
8.9%
h 26569
 
4.9%
p 25409
 
4.7%
n 25409
 
4.7%
Other values (2) 23486
 
4.4%
Uppercase Letter
Value Count Frequency (%)
E 25409
52.0%
P 22326
45.7%
S 1160
 
2.4%
Space Separator
Value Count Frequency (%)
48895
100.0%
Other Punctuation
Value Count Frequency (%)
/ 25409
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 587503
88.8%
Common 74304
 
11.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 74304
12.6%
t 73144
12.4%
o 72381
12.3%
r 72381
12.3%
a 48895
8.3%
m 48895
8.3%
i 47735
8.1%
h 26569
 
4.5%
p 25409
 
4.3%
E 25409
 
4.3%
Other values (5) 72381
12.3%
Common
Value Count Frequency (%)
48895
65.8%
/ 25409
34.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 661807
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 74304
11.2%
t 73144
11.1%
o 72381
10.9%
r 72381
10.9%
a 48895
 
7.4%
48895
 
7.4%
m 48895
 
7.4%
i 47735
 
7.2%
h 26569
 
4.0%
p 25409
 
3.8%
Other values (7) 123199
18.6%

price
Real number (ℝ)

Distinct 674
Distinct (%) 1.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 152.72069
Minimum 0
Maximum 10000
Zeros 11
Zeros (%) < 0.1%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:21.091207 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 40
Q1 69
median 106
Q3 175
95-th percentile 355
Maximum 10000
Range 10000
Interquartile range (IQR) 106

Descriptive statistics

Standard deviation 240.15417
Coefficient of variation (CV) 1.5725058
Kurtosis 585.67288
Mean 152.72069
Median Absolute Deviation (MAD) 46
Skewness 19.118939
Sum 7467278
Variance 57674.025
Monotonicity Not monotonic
2023-07-11T11:01:21.200204 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
100 2051
 
4.2%
150 2047
 
4.2%
50 1534
 
3.1%
60 1458
 
3.0%
200 1401
 
2.9%
75 1370
 
2.8%
80 1272
 
2.6%
65 1190
 
2.4%
70 1170
 
2.4%
120 1130
 
2.3%
Other values (664) 34272
70.1%
Value Count Frequency (%)
0 11
 
< 0.1%
10 17
< 0.1%
11 3
 
< 0.1%
12 4
 
< 0.1%
13 1
 
< 0.1%
15 6
 
< 0.1%
16 6
 
< 0.1%
18 2
 
< 0.1%
19 4
 
< 0.1%
20 33
0.1%
Value Count Frequency (%)
10000 3
< 0.1%
9999 3
< 0.1%
8500 1
 
< 0.1%
8000 1
 
< 0.1%
7703 1
 
< 0.1%
7500 2
< 0.1%
6800 1
 
< 0.1%
6500 3
< 0.1%
6419 1
 
< 0.1%
6000 2
< 0.1%

minimum_nights
Real number (ℝ)

SKEWED 

Distinct 109
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 7.0299622
Minimum 1
Maximum 1250
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:21.306023 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 1
median 3
Q3 5
95-th percentile 30
Maximum 1250
Range 1249
Interquartile range (IQR) 4

Descriptive statistics

Standard deviation 20.51055
Coefficient of variation (CV) 2.9175903
Kurtosis 854.07166
Mean 7.0299622
Median Absolute Deviation (MAD) 2
Skewness 21.827275
Sum 343730
Variance 420.68264
Monotonicity Not monotonic
2023-07-11T11:01:21.418139 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1 12720
26.0%
2 11696
23.9%
3 7999
16.4%
30 3760
 
7.7%
4 3303
 
6.8%
5 3034
 
6.2%
7 2058
 
4.2%
6 752
 
1.5%
14 562
 
1.1%
10 483
 
1.0%
Other values (99) 2528
 
5.2%
Value Count Frequency (%)
1 12720
26.0%
2 11696
23.9%
3 7999
16.4%
4 3303
 
6.8%
5 3034
 
6.2%
6 752
 
1.5%
7 2058
 
4.2%
8 130
 
0.3%
9 80
 
0.2%
10 483
 
1.0%
Value Count Frequency (%)
1250 1
 
< 0.1%
1000 1
 
< 0.1%
999 3
 
< 0.1%
500 5
 
< 0.1%
480 1
 
< 0.1%
400 1
 
< 0.1%
370 1
 
< 0.1%
366 1
 
< 0.1%
365 29
0.1%
364 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct 394
Distinct (%) 0.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 23.274466
Minimum 0
Maximum 629
Zeros 10052
Zeros (%) 20.6%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:21.529643 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 1
median 5
Q3 24
95-th percentile 114
Maximum 629
Range 629
Interquartile range (IQR) 23

Descriptive statistics

Standard deviation 44.550582
Coefficient of variation (CV) 1.9141398
Kurtosis 19.529788
Mean 23.274466
Median Absolute Deviation (MAD) 5
Skewness 3.6906346
Sum 1138005
Variance 1984.7544
Monotonicity Not monotonic
2023-07-11T11:01:21.635732 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 10052
20.6%
1 5244
 
10.7%
2 3465
 
7.1%
3 2520
 
5.2%
4 1994
 
4.1%
5 1618
 
3.3%
6 1357
 
2.8%
7 1179
 
2.4%
8 1127
 
2.3%
9 964
 
2.0%
Other values (384) 19375
39.6%
Value Count Frequency (%)
0 10052
20.6%
1 5244
10.7%
2 3465
 
7.1%
3 2520
 
5.2%
4 1994
 
4.1%
5 1618
 
3.3%
6 1357
 
2.8%
7 1179
 
2.4%
8 1127
 
2.3%
9 964
 
2.0%
Value Count Frequency (%)
629 1
< 0.1%
607 1
< 0.1%
597 1
< 0.1%
594 1
< 0.1%
576 1
< 0.1%
543 1
< 0.1%
540 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
480 1
< 0.1%

last_review
Date

MISSING 

Distinct 1764
Distinct (%) 4.5%
Missing 10052
Missing (%) 20.6%
Memory size 382.1 KiB
Minimum 2011-03-28 00:00:00
Maximum 2019-07-08 00:00:00
2023-07-11T11:01:21.745252 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:21.866717 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct 937
Distinct (%) 2.4%
Missing 10052
Missing (%) 20.6%
Infinite 0
Infinite (%) 0.0%
Mean 1.3732214
Minimum 0.01
Maximum 58.5
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:21.993993 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 0.01
5-th percentile 0.04
Q1 0.19
median 0.72
Q3 2.02
95-th percentile 4.64
Maximum 58.5
Range 58.49
Interquartile range (IQR) 1.83

Descriptive statistics

Standard deviation 1.680442
Coefficient of variation (CV) 1.2237225
Kurtosis 42.493469
Mean 1.3732214
Median Absolute Deviation (MAD) 0.62
Skewness 3.1301885
Sum 53340.04
Variance 2.8238853
Monotonicity Not monotonic
2023-07-11T11:01:22.099286 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0.02 919
 
1.9%
1 893
 
1.8%
0.05 893
 
1.8%
0.03 804
 
1.6%
0.16 667
 
1.4%
0.04 655
 
1.3%
0.08 596
 
1.2%
0.09 593
 
1.2%
0.06 579
 
1.2%
0.11 539
 
1.1%
Other values (927) 31705
64.8%
(Missing) 10052
 
20.6%
Value Count Frequency (%)
0.01 42
 
0.1%
0.02 919
1.9%
0.03 804
1.6%
0.04 655
1.3%
0.05 893
1.8%
0.06 579
1.2%
0.07 466
1.0%
0.08 596
1.2%
0.09 593
1.2%
0.1 457
0.9%
Value Count Frequency (%)
58.5 1
< 0.1%
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.81 1
< 0.1%
16.22 1
< 0.1%
16.03 1
< 0.1%
15.78 1
< 0.1%
15.32 1
< 0.1%

calculated_host_listings_count
Real number (ℝ)

Distinct 47
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 7.143982
Minimum 1
Maximum 327
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:22.211231 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 1
median 1
Q3 2
95-th percentile 15
Maximum 327
Range 326
Interquartile range (IQR) 1

Descriptive statistics

Standard deviation 32.952519
Coefficient of variation (CV) 4.6126262
Kurtosis 67.550888
Mean 7.143982
Median Absolute Deviation (MAD) 0
Skewness 7.9331739
Sum 349305
Variance 1085.8685
Monotonicity Not monotonic
2023-07-11T11:01:22.313593 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
Value Count Frequency (%)
1 32303
66.1%
2 6658
 
13.6%
3 2853
 
5.8%
4 1440
 
2.9%
5 845
 
1.7%
6 570
 
1.2%
8 416
 
0.9%
7 399
 
0.8%
327 327
 
0.7%
9 234
 
0.5%
Other values (37) 2850
 
5.8%
Value Count Frequency (%)
1 32303
66.1%
2 6658
 
13.6%
3 2853
 
5.8%
4 1440
 
2.9%
5 845
 
1.7%
6 570
 
1.2%
7 399
 
0.8%
8 416
 
0.9%
9 234
 
0.5%
10 210
 
0.4%
Value Count Frequency (%)
327 327
0.7%
232 232
0.5%
121 121
 
0.2%
103 103
 
0.2%
96 192
0.4%
91 91
 
0.2%
87 87
 
0.2%
65 65
 
0.1%
52 104
 
0.2%
50 50
 
0.1%

availability_365
Real number (ℝ)

ZEROS 

Distinct 366
Distinct (%) 0.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 112.78133
Minimum 0
Maximum 365
Zeros 17533
Zeros (%) 35.9%
Negative 0
Negative (%) 0.0%
Memory size 382.1 KiB
2023-07-11T11:01:22.423906 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 45
Q3 227
95-th percentile 359
Maximum 365
Range 365
Interquartile range (IQR) 227

Descriptive statistics

Standard deviation 131.62229
Coefficient of variation (CV) 1.1670575
Kurtosis -0.99753405
Mean 112.78133
Median Absolute Deviation (MAD) 45
Skewness 0.76340758
Sum 5514443
Variance 17324.427
Monotonicity Not monotonic
2023-07-11T11:01:22.531002 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 17533
35.9%
365 1295
 
2.6%
364 491
 
1.0%
1 408
 
0.8%
89 361
 
0.7%
5 340
 
0.7%
3 306
 
0.6%
179 301
 
0.6%
90 290
 
0.6%
2 270
 
0.6%
Other values (356) 27300
55.8%
Value Count Frequency (%)
0 17533
35.9%
1 408
 
0.8%
2 270
 
0.6%
3 306
 
0.6%
4 233
 
0.5%
5 340
 
0.7%
6 245
 
0.5%
7 219
 
0.4%
8 233
 
0.5%
9 193
 
0.4%
Value Count Frequency (%)
365 1295
2.6%
364 491
 
1.0%
363 239
 
0.5%
362 166
 
0.3%
361 111
 
0.2%
360 102
 
0.2%
359 135
 
0.3%
358 180
 
0.4%
357 95
 
0.2%
356 78
 
0.2%

Interactions

2023-07-11T11:01:16.743478 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:08.569789 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.469804 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.533989 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.437097 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.312855 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.171259 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.047690 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.945313 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.840681 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.840295 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:08.661871 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.713527 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.631369 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.532285 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.402683 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.262887 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.141876 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.036807 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.937463 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.932118 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:08.751221 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.797043 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.719655 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.620070 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.488129 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.350047 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.228092 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.125107 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.024736 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:17.018532 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:08.835676 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.883872 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.802313 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.704350 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.568239 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.433239 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.313004 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.221617 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.110635 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:17.110894 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:08.924455 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.973775 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.891252 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.788908 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.653264 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.521515 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.400659 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.310457 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.199005 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:17.199413 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.010351 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.062650 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.976798 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.873099 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.734586 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.605319 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.488715 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.394521 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.286399 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:17.291946 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.100978 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.154541 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.066425 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.959839 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.821612 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.692940 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.578186 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.484030 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.376261 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:17.384461 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.189936 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.246731 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.154817 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.045964 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.909138 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.780162 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.670210 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.572050 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.466205 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:17.476842 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.280083 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.342876 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.252735 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.134059 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.994603 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.869102 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.760439 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.659484 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.559422 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:17.569512 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:09.370407 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:10.438339 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:11.344400 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:12.221419 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.082154 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:13.957617 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:14.853653 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:15.748682 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-11T11:01:16.649086 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-11T11:01:22.627157 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
id host_id latitude longitude price minimum_nights number_of_reviews reviews_per_month calculated_host_listings_count availability_365 neighbourhood_group room_type
id 1.000 0.559 0.005 0.071 -0.021 -0.058 -0.308 0.360 0.135 0.166 0.064 0.070
host_id 0.559 1.000 0.050 0.109 -0.072 -0.130 -0.128 0.268 0.147 0.173 0.100 0.092
latitude 0.005 0.050 1.000 0.035 0.136 0.022 -0.044 -0.023 0.004 -0.007 0.539 0.117
longitude 0.071 0.109 0.035 1.000 -0.438 -0.119 0.080 0.119 0.064 0.069 0.654 0.158
price -0.021 -0.072 0.136 -0.438 1.000 0.101 -0.055 -0.019 -0.106 0.086 0.018 0.025
minimum_nights -0.058 -0.130 0.022 -0.119 0.101 1.000 -0.175 -0.289 0.064 0.076 0.003 0.012
number_of_reviews -0.308 -0.128 -0.044 0.080 -0.055 -0.175 1.000 0.706 0.056 0.237 0.027 0.021
reviews_per_month 0.360 0.268 -0.023 0.119 -0.019 -0.289 0.706 1.000 0.146 0.392 0.048 0.029
calculated_host_listings_count 0.135 0.147 0.004 0.064 -0.106 0.064 0.056 0.146 1.000 0.407 0.089 0.097
availability_365 0.166 0.173 -0.007 0.069 0.086 0.076 0.237 0.392 0.407 1.000 0.083 0.087
neighbourhood_group 0.064 0.100 0.539 0.654 0.018 0.003 0.027 0.048 0.089 0.083 1.000 0.126
room_type 0.070 0.092 0.117 0.158 0.025 0.012 0.021 0.029 0.097 0.087 0.126 1.000

Missing values

2023-07-11T11:01:17.712287 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-11T11:01:17.938933 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-11T11:01:18.121987 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id name host_id host_name neighbourhood_group neighbourhood latitude longitude room_type price minimum_nights number_of_reviews last_review reviews_per_month calculated_host_listings_count availability_365
0 2539 Clean & quiet apt home by the park 2787 John Brooklyn Kensington 40.64749 -73.97237 Private room 149 1 9 2018-10-19 0.21 6 365
1 2595 Skylit Midtown Castle 2845 Jennifer Manhattan Midtown 40.75362 -73.98377 Entire home/apt 225 1 45 2019-05-21 0.38 2 355
2 3647 THE VILLAGE OF HARLEM....NEW YORK ! 4632 Elisabeth Manhattan Harlem 40.80902 -73.94190 Private room 150 3 0 NaN NaN 1 365
3 3831 Cozy Entire Floor of Brownstone 4869 LisaRoxanne Brooklyn Clinton Hill 40.68514 -73.95976 Entire home/apt 89 1 270 2019-07-05 4.64 1 194
4 5022 Entire Apt: Spacious Studio/Loft by central park 7192 Laura Manhattan East Harlem 40.79851 -73.94399 Entire home/apt 80 10 9 2018-11-19 0.10 1 0
5 5099 Large Cozy 1 BR Apartment In Midtown East 7322 Chris Manhattan Murray Hill 40.74767 -73.97500 Entire home/apt 200 3 74 2019-06-22 0.59 1 129
6 5121 BlissArtsSpace! 7356 Garon Brooklyn Bedford-Stuyvesant 40.68688 -73.95596 Private room 60 45 49 2017-10-05 0.40 1 0
7 5178 Large Furnished Room Near B'way 8967 Shunichi Manhattan Hell's Kitchen 40.76489 -73.98493 Private room 79 2 430 2019-06-24 3.47 1 220
8 5203 Cozy Clean Guest Room - Family Apt 7490 MaryEllen Manhattan Upper West Side 40.80178 -73.96723 Private room 79 2 118 2017-07-21 0.99 1 0
9 5238 Cute & Cozy Lower East Side 1 bdrm 7549 Ben Manhattan Chinatown 40.71344 -73.99037 Entire home/apt 150 1 160 2019-06-09 1.33 4 188
id name host_id host_name neighbourhood_group neighbourhood latitude longitude room_type price minimum_nights number_of_reviews last_review reviews_per_month calculated_host_listings_count availability_365
48885 36482809 Stunning Bedroom NYC! Walking to Central Park!! 131529729 Kendall Manhattan East Harlem 40.79633 -73.93605 Private room 75 2 0 NaN NaN 2 353
48886 36483010 Comfy 1 Bedroom in Midtown East 274311461 Scott Manhattan Midtown 40.75561 -73.96723 Entire home/apt 200 6 0 NaN NaN 1 176
48887 36483152 Garden Jewel Apartment in Williamsburg New York 208514239 Melki Brooklyn Williamsburg 40.71232 -73.94220 Entire home/apt 170 1 0 NaN NaN 3 365
48888 36484087 Spacious Room w/ Private Rooftop, Central location 274321313 Kat Manhattan Hell's Kitchen 40.76392 -73.99183 Private room 125 4 0 NaN NaN 1 31
48889 36484363 QUIT PRIVATE HOUSE 107716952 Michael Queens Jamaica 40.69137 -73.80844 Private room 65 1 0 NaN NaN 2 163
48890 36484665 Charming one bedroom - newly renovated rowhouse 8232441 Sabrina Brooklyn Bedford-Stuyvesant 40.67853 -73.94995 Private room 70 2 0 NaN NaN 2 9
48891 36485057 Affordable room in Bushwick/East Williamsburg 6570630 Marisol Brooklyn Bushwick 40.70184 -73.93317 Private room 40 4 0 NaN NaN 2 36
48892 36485431 Sunny Studio at Historical Neighborhood 23492952 Ilgar & Aysel Manhattan Harlem 40.81475 -73.94867 Entire home/apt 115 10 0 NaN NaN 1 27
48893 36485609 43rd St. Time Square-cozy single bed 30985759 Taz Manhattan Hell's Kitchen 40.75751 -73.99112 Shared room 55 1 0 NaN NaN 6 2
48894 36487245 Trendy duplex in the very heart of Hell's Kitchen 68119814 Christophe Manhattan Hell's Kitchen 40.76404 -73.98933 Private room 90 7 0 NaN NaN 1 23